This dataset presents the spatiotemporal evolution of the Curve Number (CN) in the experimental Ichu River basin, located in the central Peruvian Andes, from 2017 to 2023. CN maps were generated by integrating satellite data (Sentinel-2) and soil models (SoilGrid), applying the GPCN10 methodology for automated CN value assignment. Additionally, annual comparative analyses highlight variations in the basin's hydrological response, linked to both land-use changes and climate variability. The dataset includes graphical comparisons of CN differences between consecutive years (e.g., 2017 vs. 2018), revealing areas with significant changes in soil infiltration capacity. Furthermore, change class maps identify regions undergoing land degradation (due to urbanization or deforestation) and areas showing improvements in water retention, associated with conservation practices such as reforestation. These data provide a foundation for assessing the impact of land-use changes on surface runoff and contribute to sustainable water resource management strategies in the region.This dataset contains the spatiotemporal evolution of Curve Number (CN) in the Ichu River Basin (2017-2023), including: Annual CN distribution maps at 10-meter resolution. Direct pixel-by-pixel CN differences between consecutive years. Change class maps identifying areas with significant land-use variations.All data were derived using Sentinel-2 imagery, SoilGrid datasets, and the GPCN10 methodology. These datasets are intended to support hydrological modeling, land-use change assessments, and climate impact analysis in Andean watersheds.

Curve Number Distribution Maps (Map_CN_date.pdf):These maps illustrate the spatial distribution of the Curve Number parameter in the experimental basin at a 10-meter resolution for the specified years (2017-2023). The Curve Number is a crucial parameter in hydrological modeling, influencing runoff estimation and soil infiltration capacity.The dataset includes both the visual representations in PDF format and the corresponding GIS-compatible shapefile database (.shp, .dbf, .shx, .prj) for each year. These files allow users to: Identify hydrologically sensitive areas. Analyze the evolution of land cover changes (e.g., deforestation, reforestation, urbanization). Compare CN trends across different hydrological periods. These datasets are valuable for watershed management, climate change impact studies, and hydrological modeling applications.Direct Pixel Differences (Map_dif_date.pdf):These figures display the pixel-by-pixel differences in Curve Number values between two consecutive years, enabling the identification of areas that have undergone significant changes in their hydrological properties. The changes in CN values can be attributed to: Deforestation and soil degradation (leading to an increase in runoff potential). Urban expansion (modifying permeability and increasing impervious surfaces). Reforestation or conservation practices (reducing runoff and increasing infiltration). Alongside these visualizations, the dataset includes GIS-compatible shapefiles (.shp, .dbf, .shx, .prj) for each year, allowing users to conduct further spatial analyses, overlay additional environmental data, or integrate with other hydrological models. These datasets provide a quantitative basis for evaluating the impact of land-use dynamics on the hydrological response of the basin, supporting decision-making processes in watershed conservation, flood risk assessment, and sustainable land management.Change Class Maps (Map_change_class_date.pdf)These figures represent the classification of changes in Curve Number (CN) values for the indicated periods (2017-2023). The classification includes categories such as High Decrease, Low Decrease, No Change, Low Increase, and High Increase, which highlight areas with significant variations due to land-use changes, soil degradation, conservation efforts, or urban expansion.Additionally, the shapefile database (.shp, .dbf, .shx, .prj) for each year is provided, containing the geospatial representation of these classifications. This allows researchers to perform custom analyses, integrate the data into GIS platforms, and cross-reference with other hydrological or land-use datasets.By analyzing these maps and shapefiles, researchers can assess the spatiotemporal trends in hydrological response and evaluate the impact of land management strategies on runoff generation.